Systems and associated methods for use of patterns in processing on mobile monitoring device

ABSTRACT

An arrangement may include a first system provided for processing physiological data representative of a beating heart. The first system may be adapted to execute a process for using at least one pattern to detect a notable finding in the physiological data and for sending the notable finding to a second system. The second system may be adapted to execute a process for analyzing the notable finding, for determining at least one new pattern to send to the first system, and for sending the at least one new pattern to the first system. The at least one new pattern may also include a rule that includes a set of conditions and an action to perform if the set of conditions is met.

RELATED APPLICATIONS

This application is a continuation application and claims the benefitunder 35 U.S.C. § 120 of U.S. application Ser. No. 15/253,401, now U.S.Pat. No. 9,770,181, filed on Aug. 31, 2016 and titled Systems andAssociated Methods for Use of Patterns in Processing on MobileMonitoring Device which, in turn, is a continuation of U.S. applicationSer. No. 14/592,581, now U.S. Pat. No. 9,445,736 filed on Jan. 8, 2015titled Use of Patterns in Processing on Mobile Monitoring Device andComputer System which, in turn, is a continuation of U.S. applicationSer. No. 13/525,503, now U.S. Pat. No. 8,954,137 filed on Jun. 18, 2012titled Use of Patterns in Processing on Mobile Monitoring Device andComputer System which, in turn, is a continuation of U.S. applicationSer. No. 11/136,338, now U.S. Pat. No. 8,204,580 filed on May 24, 2005titled Use of Patterns in Processing on Mobile Monitoring Device andComputer System which, in turn, claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Patent Application Ser. No. 60/574,268 filedon May 25, 2004 and titled Wireless ECG Mobile Device that Communicateswith a Base Station, the entire contents of which are incorporatedherein by reference.

FIELD OF THE INVENTION

The present invention relates to systems and methods for monitoringphysiological characteristics of ambulatory patients.

BACKGROUND

In Holter monitoring, a patient module acquires and records ECG data butdoes not analyze the data. After recording is complete, data istransferred from the patient module to a base station that analyzes thedata. The base station identifies clinically notable findings for reviewby clinical specialists. In “event monitoring” data selection isperformed in the patient module, either as the result of ECG analysis ordue to a patient-initiated trigger. Selected ECG results are transmittedor downloaded to a system at a central facility.

U.S. Pat. No. 6,694,177 B2 by Eggers et al. entitled Control Of DataTransmission Between A Remote Monitoring Unit And A Central Unitdescribes bidirectional communications between a remote monitoring unitand a central unit. The remote monitoring unit obtains a monitored dataset from the patient, analyzes the monitored data set to obtain aderived data set and determines from the derived data set thatcommunication with the central unit is required. The central unitanalyzes the initially transmitted data set and instructs the remotemonitoring unit to transmit an additional data set related to themonitored data set and a time when to transmit the additionaltransmitted data set.

This background information is provided to reveal information believedby the applicant to be of possible relevance to the present invention.No admission is necessarily intended, nor should be construed, that anyof the preceding information constitutes prior art against the presentinvention.

SUMMARY OF THE INVENTION

With the above in mind, embodiments of the present invention are relatedto a system that may include circuitry to receive information from afirst system including physiological data such as ECG datarepresentative of a beating heart, circuitry to analyze thephysiological data using at least one pattern to detect a notablefinding in the physiological data, circuitry to determine at least onepattern to send to the first system based on the analysis of thephysiological data, and circuitry to send the at least one determinedpattern to the first system.

The following are within the scope of the claim. The determined patternmay include the parameters of a mathematical model. The determinedpattern may include a template derived from historical physiologicaldata. The circuitry to analyze the physiological data may includecircuitry to compare the analyzed physiological data to templates fromcategories of physiological events in order to classify thephysiological data. The determined pattern may include sending software.The pattern may be a cardiac rhythm pattern. The cardiac rhythm patternmay be a “bigeminy” rhythm that may be defined as having alternatingnormal “N” and ventricular “V” beats, the pattern including a rhythmtemplate that may represent the bigeminy pattern as eight (8) beats of(N V N V N V N V) and wherein incoming beats may be compared to thebigeminy template to determine if a bigeminy condition exists in signalsof the incoming beats.

The pattern may be selected based on information from the first system,including user characteristics or physiological data samples collectedfor the user from the first system, or data derived by the first systemfrom the physiological data samples, including heart beat patterns andheart rhythm patterns. The pattern may be selected based on usercharacteristics, including patient clinical and demographic information,and may be sent to initialize the first system. The pattern may be newor updated software for execution by the first system. The pattern maybe a rule that has a set of conditions and an action to perform if theset of conditions is met. The rule may be a classification rule. Therule may be a processing rule. The set of rules may be generated for aspecific condition or patient.

According to an additional aspect of the invention, an arrangement mayinclude a first system for processing physiological data such as ECGdata representative of a beating heart using at least one pattern todetect a notable finding in the physiological data, sending thephysiological data corresponding to the notable finding to a secondsystem, and the second system executing a process for analyzing thephysiological data corresponding to the notable finding and determiningat least one new pattern to send to the first system based on theanalysis of the physiological data. The arrangement also may include aprocess to send the at least one determined pattern to the first system.

According to an additional aspect of the invention, a method may includereceiving information from a first system including physiological datasuch as ECG data representative of a beating heart, analyzing thephysiological data using at least one pattern to detect a notablefinding in the physiological data, determining at least one pattern tosend to the first system based on analyzing the physiological data, andsending the at least one determined pattern to the first system.

A pattern may be a specification of characteristics, i.e., a form ormodel that may be used by the first system and the second computersystem to compare to incoming physiological data. For example, andwithout limitation, a pattern may include new or updated software forexecution by the first system. Patterns may be represented as events intime. A pattern may be any specification of characteristics or a set ofparameters. A pattern may be a rule that has a set of conditions and anaction that may be performed if the set of conditions is met. Patternsmay be used to affect subsequent processing by the first system. Thus,the second computer system may modify operation of the first system bychanging new or revised patterns.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system for patient monitoring andanalysis.

FIG. 2 is a block diagram of a mobile device.

FIG. 3 is a flow chart detailing aspects of cooperative processing on amobile device.

FIG. 4 is flow chart detailing aspects of cooperative processing on asecond system.

FIG. 5 is a block diagram of computing functions performed by the secondcomputer system.

DETAILED DESCRIPTION OF THE INVENTION

The present invention will now be described more fully hereinafter withreference to the accompanying drawings, in which preferred embodimentsof the invention are shown. This invention may, however, be embodied inmany different forms and should not be construed as limited to theembodiments set forth herein. Rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the invention to those skilled in the art. Those ofordinary skill in the art realize that the following descriptions of theembodiments of the present invention are illustrative and are notintended to be limiting in any way. Other embodiments of the presentinvention will readily suggest themselves to such skilled persons havingthe benefit of this disclosure. Like numbers refer to like elementsthroughout.

Although the following detailed description contains many specifics forthe purposes of illustration, anyone of ordinary skill in the art willappreciate that many variations and alterations to the following detailsare within the scope of the invention. Accordingly, the followingembodiments of the invention are set forth without any loss ofgenerality to, and without imposing limitations upon, the claimedinvention.

In this detailed description of the present invention, a person skilledin the art should note that directional terms, such as “above,” “below,”“upper,” “lower,” and other like terms are used for the convenience ofthe reader in reference to the drawings. Also, a person skilled in theart should notice this description may contain other terminology toconvey position, orientation, and direction without departing from theprinciples of the present invention.

Furthermore, in this detailed description, a person skilled in the artshould note that quantitative qualifying terms such as “generally,”“substantially,” “mostly,” and other terms are used, in general, to meanthat the referred to object, characteristic, or quality constitutes amajority of the subject of the reference. The meaning of any of theseterms is dependent upon the context within which it is used, and themeaning may be expressly modified.

Referring to FIG. 1, a system arrangement 10 may include physiologicalsensors 16 connected to a mobile patient device 12 that may collect,process, and record findings from physiological data that may beprovided from the sensors 16. The mobile patient device 12 may include amobile processing device 20 that may execute a process 40 thatdetermines notable findings in real time and sends the notable findingsover a communication link 28 to a second computer system 24. Forexample, and without limitation, the second computer system 24 mayinclude computing resources that allow it to reprocess the physiologicaldata corresponding to the notable findings with greater accuracy thanthe mobile device 12. The second computer system 24 may produce a reportand may send the results for clinical review (not shown).

The second computer system 24 may analyze differences between theresults from the mobile device 12 and the results from the reprocessingby the second computer system 24, and may generate or retrieve new“patterns” (discussed below) to improve processing on the mobile device12. The new patterns may be sent over the communications link 28 to themobile device 12. The second system 24 may work cooperatively with themobile device 12 to provide the high quality and timely detection ofnotable findings.

The sensors 16 may be attached to the body of the patient (or may beembedded in clothing next to the skin). The sensors 16 may providephysiological signals to a front-end 18 of the mobile device thattypically may include one or more amplifiers, one or more filters, andone or more A/D converters. The mobile device 12 typically may be wornor carried on the body of the patient. The front end 18 may amplify andfilter the analog signals and convert the analog signals to digital datafor processing by a processing device 20 in the mobile device 12. Thesystem arrangement 10 may be implemented in several differentconfigurations as discussed below.

Communications between the second computer system 24 and mobile device12 may be bi-directional. The mobile device 12 may record, analyzeand/or report on patient physiological data, such as electro-cardiograph(ECG), blood pressure, respiration, temperature, EEG (brain waves),electromyography, etc. For example, and without limitation, the mobiledevice 12 may be configured to detect cardiac abnormalities in an ECGsignal.

The second computer system 24 may maintain a large database 26 ofphysiologic patterns. The database 26 may store patterns, referencetemplates, and parameters for use in beat and rhythm classification bythe mobile device 12 or second computer system 24. The second computersystem 24 may maintain historical reference data on the subject,including for example, and without limitation, ECG strips, templates andreference data for future processing.

Initialization software to enable the mobile device 12 to communicatewith the second computer system 24 may be downloaded to the mobiledevice 12. The second computer system 24 may configure the mobile device12, and may provide software, parameters and reference data for use bythe mobile device 12. The installation may take into considerationexisting hardware and software configurations of the mobile device 12.The initial download may be, for example, and without limitation, a“Setup.exe” type application that selects and manages download of othercomponents in the downloaded software. The second computer system mayalso transmit software updates to the mobile device.

Referring to FIG. 2, the mobile patient device 12 may include threefunctional components, as shown: a front end 18 that may condition anddigitize signals sensed by the mobile device from a patient; aprocessing device 20 and associated support hardware to produce acomputing device that may process the signals based on software andalgorithms; and a communications path 28, such as a wireless link, thatmay allow the processor to communicate with the second computer system.The components may be grouped into a single device or may be configuredas two or three separate devices. A patient may wear electrode sensors(FIG. 1) that may be attached to a lightweight patient cable assembly(not numbered). The other end of the cable may be attached to a compactcard which may plug into a standard type slot, such as a compact flashslot or PCMCIA slot, on a computer.

For example, and without limitation, the card may include inputprocessing circuits (front end 18) to amplify, filter and digitize theinput signals from the electrode sensors, and also non-volatile flashmemory 21 to store the ECG and other data. The card may plug into astandard slot on the processing device 20, for example, and withoutlimitation, a PDA or handheld PC, which may perform the processing andcommunicate with the second computer system 24 over established wirelessnetworks. In another embodiment, (not shown) the patient may wear agarment with built in sensors and a built-in front end 18 (e.g.,amplifiers, filters, and analog to digital converters), for example, andwithout limitation, integrated ECG sensors that may permit lessintrusive monitoring than conventional stick-on electrodes. Thegarment-based sensors may have a known and predictable electrodeconfiguration. Garments with ECG sensors may have an associatedidentifier that may specify the ECG sensor and lead configuration. Theconfiguration identifier may be encoded into the garment to allow it tobe determined automatically by the patient module or base station. Theidentifier may use an encoding technology such as radio-frequencyidentification (RFID) or an electrically accessed circuit incorporatedinto the garment. Once the identifier is known, the mobile device 12 andthe second system 24 may adapt the processing to the sensorconfiguration corresponding to the identifier.

Also for example, and without limitation, the front-end 18 maycommunicate with a mobile phone and/or computer that may be powered by along-life battery or fuel cell. The mobile phone/computer may have astandard mobile operating system on it that may allow the patient to runstandard applications. The mobile phone/computer may be initialized forthe patient mobile device function by downloading an application thatmay configure the mobile phone and/or computer so that either or bothdevices may communicate with the front end and communicate over thewireless mesh or Internet.

Front End

The front end 18 may interface to the physiological sensors and may makeavailable a stream of digital data. The sensors to which the front end18 connects may include conventional sensors such as ECG electrodes, oradvanced technology such as a garment that incorporates sensors into thefabric, as discussed above. For garment-based sensors, the front end mayalso be incorporated into the garment. The front end may be separatefrom the mobile computer, or the front end 18 may communicate with themobile computer by wire or by a wireless connection such as Bluetooth.The front end may receive power from the mobile processor over a wire.The front end, when connected wirelessly, may save power andtransmission costs by storing signals and then regularly transmittingthe stored signals during a brief transmission interval. The front endmay include memory to store digitized signals.

Mobile Processing Device

The mobile processing device 20 may analyze the signals and maycommunicate with the second computer system through the wireless link.The mobile processing device may be coupled to the front end 18. Themobile device 12 may include memory 21, including non-volatile memorysuch as flash memory, that may store the programs that run in the deviceand the digitized signals. The mobile processing device 20 and secondcomputer system 24 may use a secure communication protocol or mayencrypt data exchanged between the mobile computer and second computersystem 24. The mobile processing device 20 and second computer system 24may use efficient data streaming protocols to minimize network traffic,instead of protocols such as TCP/IP. The mobile processing device 20 maybe a PDA or a wireless phone. The mobile processing device 20 may be ormay include a specialized processor that may optimize signal-processingcapabilities of the mobile device 12, such as a digital signal processorunit. The mobile processing device 20 may provide a user interfacethrough which the user can interact with the mobile device, e.g., themobile computer may be accessible using a browser. The user may reviewsettings and may modify operation of the mobile device.

Communication Path

The communications path 28, such as a wireless link, may providebi-directional connectivity that may allow the mobile device 12 tofunction as a networked computer. The wireless link 28 may use theInternet to communicate with the second computer system 24 or a wirelessmesh to communicate to the second computer system 24. The wireless linkmay be integrated as part of a mobile phone or PDA.

The mobile device may record, analyze, and report on spatial parameterssuch as the patient's motion, physical orientation, and location asdetermined by global positioning systems in the device (not shown, butGPS may be common in a cell phone, for example). The mobile device mayuse the spatial parameters to determine whether a change inphysiological data may be attributed to a change in physical orientationof the patient. The mobile device may use the signals from motion andphysical orientation to detect that a patient has fallen down. Themobile device may be configured to report that event to the mobiledevice facility immediately. Prior to reporting, the mobile device maygive the patient time to prevent the report, and may prompt the patientwith a cue (e.g. sound, vibration) that, if responded to, may cancel thereport.

The mobile device 12 may allow the patient to mark an occurrence of asymptom, including onset/offset, or to document activity. The mobiledevice may allow the patient to add a voice note to each mark, or toproduce a mark by adding a voice note. The mobile device may be used bythe patient to manually trigger a request for help. When reporting apotential emergency, the mobile device may communicate directly with afacility or system other than the second computer system.

Patterns

A pattern, as used herein for purposes of definition, may be aspecification of characteristics, i.e., a form or model that may be usedby the mobile device 12 and the second computer system 24 to compare toincoming ECG data. In some cases, a pattern may include new or updatedsoftware for execution by the mobile device 12. An exemplary patterninvolving a model and specification of characteristics may be thepattern of a single heart beat (e.g., a “heart beat pattern”).

The heart beat pattern may be represented by QRS samples of the signal,where each sample may be represented according to amplitude (relative toa reference level) and time of occurrence of a point in the pattern(relative to some point in the beat). For example, and withoutlimitation, if the time of the Q peak is considered time zero, then thetime for R is the time from Q to R. These relative measures may allowthe heart beat pattern to be compared to newly detected beats, and todetect clinically significant episodes or events.

The heart beat pattern and other parameters, such as the beat width, maymake up a “beat template,” which may be derived from a number of similarbeats. A beat template may be produced for each beat category (e.g.,normal, ventricular, etc.). Newly detected beats may be compared to thebeat templates from each beat category in order to classify the beats.For example, and without limitation, comparing a beat to a beat templatemay be a type of pattern matching.

Another type of pattern matching may be used for cardiac rhythmdetection. The rhythm called bigeminy may be defined as havingalternating normal “N” and ventricular “V” beats (N V N V V . . . ). Arhythm template may represent bigeminy as eight (8) beats: (N V N V N VN V). Incoming beats may be compared to the bigeminy template to see ifa bigeminy condition exists in the incoming beats.

Patterns may be represented as events in time, such as the QRS waveformor the pattern of beats described above. However, a pattern may not haveto be described according to time. A pattern may be any specification ofcharacteristics or a set of parameters. For example, an RR interval maybe classified according to the “pattern” of its length. To performpattern matching and classification of a signal, pattern matchingcriteria may be defined. Thus, in the above rhythm example, a beat maybe classified as being either N (normal), V (ventricular), or possiblysomething else like fusion (in which case it would not match the givenbigeminy rhythm).

Another type of pattern may be characterized as a rule. A rule may havea set of conditions and an action that is to be performed if the set ofconditions is met. For example, a beat classification rule may be asfollows: “if a beat is wide, classify it as ventricular.” In thisembodiment, the condition is “wide beat,” and the action is to “classifybeat as ventricular.” This is an example of a classification rule. Othertypes of rules may include processing rules such as, for example, andwithout limitation, a rule to determine when to escalate a cardiacfinding on the mobile device 12 and immediately notify the second system24. A set of rules may be generated, e.g., by the second system for aspecific condition or patient. At least a subset of those rules may bedownloaded to the mobile device 12 for a specific condition or patient.The rules may affect subsequent processing by the mobile device 12.

Referring to FIG. 3, a cooperative processing process 40 that occurs onthe mobile device 20 is shown. The mobile device (FIG. 1) may acquire 42physiological data from the front-end 18. The process 40 may perform adetailed analysis 44 of the data to detect an episode of somephysiological event. If an episode was detected, the process 40 maydetermine 46 if the episode may be considered as a notable finding. Forexample, and without limitation, notable findings may include an episodewhich has been classified as a clinically important finding (such asatrial fibrillation); an episode that contains too much noise for themobile device to successfully analyze; or a pattern that does not fitany of the templates in the mobile device. If the episode is not anotable finding, the process 40 may begin again. If the episode isconsidered a notable finding, the process 40 may send 48 thephysiological data corresponding to the notable finding to the secondsystem 24. The physiological data may be sent 48 either immediatelyafter the notable finding was determined, or multiple notable findingsmay be stored and sent periodically.

For example, and without limitation, if the device 20 is to detect heartarrhythmias, then the physiological data may be ECG, and the detailedanalysis 44 may be a cardiac arrhythmia detector. Notable findings forcardiac arrhythmias may include ventricular flutter, ventricularfibrillation, and/or atrial fibrillation. If a detected arrhythmia isdetermined to be a notable finding because it has been classified as,for example, atrial fibrillation, then the ECG corresponding to theepisode of atrial fibrillation (i.e. 2 minutes of ECG) may be sent 48 tothe second system 24. For example, and without limitation, if theepisode of atrial fibrillation is 25 beats long, then a segment of ECGtwo minutes long that may include the 25 beats may be sent 48 along withan indication of the onset and offset of atrial fibrillation within theselected ECG.

The data corresponding to the notable finding sent in 48 may be datagenerated by different parts of the process 40. The example describessending the ECG, which may be the raw physiological data input to theprocess 40. The input data may be sent so that the second system 24 mayreprocess the same data with its better resources (e.g., more processingpower and/or storage capacity). The process 40 also may sendintermediate or final results of its analysis of the data, so that thesecond system 24 may improve the processing of the process 40, asfollows.

The detailed analysis 44 may detect episodes of events using patterns,which could be, for example, and without limitation, beat templates,rhythm templates, and/or rules, as described above. These patterns maybe stored on the processing device 20 in the pattern database 58. Newpatterns may be received 52 on the mobile device 20 from the secondsystem 24. The new patterns may be stored 54 in the pattern database 58to be used by the detailed analysis 44.

Referring to FIG. 4, cooperative processing 60 that may occur on thesecond system 24 is shown. For example, and without limitation, theprocess 60 may receive data 62 from the mobile device 20, such as theraw physiological data that may be input to the mobile device 20. Inaddition, the data may include intermediate or final results of analysison the mobile device 12. The data may be reprocessed 64 using moreextensive resources than are available on the mobile device 12, such asfor example and without limitation, a variety of algorithms 66, morepowerful processors (not shown), and/or a larger set of databases 68.The reprocessed data may be compared 70 to the data received from themobile device 12. If there are no significant differences, then themobile device 12 may not be updated.

If significant differences are found, then the reprocessed results onthe second computer system may be assumed more accurate, and thoseresults may be used to generate 72 new patterns for the mobile device12. The new patterns may be used to improve processing on the mobiledevice, such as for example, and without limitation, to make processingmore accurate and/or more efficient; and/or to reduce communication timewith the second computer system. The new patterns may be sent 74 to themobile device 20, and may be stored in the databases 68 along with anyrelevant data, such as the raw physiological data that may be receivedfrom the mobile device 12.

As an example of the type of processing that may occur on the secondsystem 24, assume again that the system 10 is a cardiac arrhythmiadetector. The data received 62 from the mobile device may be ECG data aswell as an indication of the onset and offset of the particulararrhythmia. Suppose the mobile device sends two minutes of ECG data withan indication of 25 beats of atrial fibrillation, and the reprocessing64 also detects atrial fibrillation, but for a period of 10 beats. Thecomparison 70 may show a significant difference, and a new pattern maybe generated 72 and sent 74 to the mobile device 20.

The databases 68 may store data for the specific patient wearing theportable device, and data that may have been compiled from manypatients. For example, and without limitation, the databases may containphysiological data, derived data, rules, procedures, programs, and/ortemplates.

If the second system 24 detects a notable finding from the data sent bythe mobile device 12, the system 24 may perform several actions (notshown) in addition to generating new patterns, including notifyingclinical personnel and/or the patient of determined clinically notablefindings. Some notifications may be low priority, and may be carried outin the form of daily updates to clinical personnel for review. If thesecond system 24 detects a more serious finding, the second system 24may make the notification immediately, and may contact a physician oremergency services directly instead of the clinical review personnel.

The process 60 may include sending a new pattern to modify the mobiledevice's processing. Based on the analysis of the data, the secondcomputer system 24 may direct the mobile device 12 to send additionaldata or to modify the operation of the mobile device 12 by changing whatthe mobile device 12 looks for, or to improve the processing byproviding more appropriate reference data, processing rules, or new orrevised patterns of other kinds.

Thus, processing workload may be split between the mobile device 12 andthe second computer system 24. The balance may be adjusted to suitavailable mobile technology. For example, and without limitation, themobile processor 20 may be a pre-processor for the second computersystem 24. The mobile device 12 may receive operating parameters andreference data for algorithms executed on the mobile device 12, as wellas executable code from the second computer system 24. The mobile device12 may operate in standalone mode when access to the second computersystem 24 is not available. During that time, the mobile device 12 maysave notable findings and information in non-volatile memory. In typicaloperation, the mobile device 12 periodically rather that constantly maycommunicate data to the second computer system 24. More specifically,the mobile device may send periodic updates of information to the secondcomputer system 24. Immediate contact with the second computer system 24may occur only if there is a potentially serious event, or an event thatneeds the additional processing resources of the second computer system24, rather than a notable finding.

Under normal operating conditions, the second computer system 24 may bein regular contact with the mobile device 12. Even when the mobiledevice 12 does not detect any findings that herald a possible problem,the mobile device 12 may send (or be asked to send) data. The data maybe analyzed by the second computer system 24, which may have access togreater computational and/or database resources than the mobile device12. If the second computer system 24 finds errors, it may send themobile device 12 additional reference data and parameters to correct theprocessing, as discussed above. The intervals between data transmissionmay be controlled by the second computer system 24, and may take intoaccount the clinical risk of the patient and/or the complexity of thesignals being processed by the mobile device 12.

During cooperative processing, the selection of findings to look for andthe threshold for detecting clinically notable findings may be governedand modified, as needed, by the second computer system 24. The secondcomputer system 24 may conduct a detailed and thorough analysis that mayuse, for example, and without limitation, state of the art ECG analysisof the signals to determine whether the data may include a notablefinding. A final examination and classification of the data may beperformed by the second computer system 24. The second computer system24 may determine a new pattern to send to the mobile device 12, based onthe analysis, and may send the new pattern to the mobile device 12.

The mobile device 12 and second computer system 24 may workcooperatively and collaboratively as a hybrid of distributed processing.Cooperative processing may attempt to strike a balance between theprocessing requirements on the mobile device 12 and the amount of datathat is sent to the second computer system 24 versus the need to performhigh quality analysis.

Cooperative processing in the context of ECG analysis may be a hybridbetween so called “Holter monitoring” and “event monitoring.” In “Holtermonitoring,” a patient module may acquire the ECG data but may notanalyze the data. After recording is complete, the data may be analyzedat a base station. The base station may identify clinically notablefindings for review by clinical specialists. In “event monitoring,” dataselection may be performed in the patient module, either as the resultof ECG analysis or due to a patient-initiated trigger. The selected ECGresults may be transmitted or downloaded to a system at a centralfacility. In contrast, in cooperative processing, both the mobile device12 and the second computer system 24 may play a role in processingacquired data and, as discussed either, may raise an alert to thepatient or send a notification to a central facility.

Clinical Performance

The role of the mobile device 12 may be to advantageously exhibitrelatively good sensitivity to potential events and not be toosusceptible to false negatives; whereas the role of the second computersystem 24 may be to advantageously improve the positive predictioncapabilities of the system 10 by rejecting false positives, withoutdecreasing the sensitivity of the system 10 by rejecting true positives.

Configurability

The system arrangement 10 may be configurable so that medical personnelcan determine what types of episodes will be reported. The thresholdsfor reporting may be set low for a new patient, such that almost anyabnormality may be reported. For patients with known abnormalities, thethresholds may be set to report and send the ECG data only when theabnormality is more severe than prior episodes of the same abnormality.

Reference Data

The second computer system 24 may send reference population data andparameters to the mobile device 12 as the cooperative processingexecutes in the mobile device 12 to make decisions as it processes theincoming ECG data. The reference data and parameters may be sent at thestart of monitoring or at any point during the monitoring.

The reference data may include some or all of the following information:

Parameters such as probabilities, rates, interval durations andamplitudes;

Templates of individual beat morphologies, as discussed below andsequences of beat types (such as the bigeminy pattern discussed above).

The reference data may be indexed according to characteristics, such asage, gender, height or weight. The second computer system may take intoconsideration the patient's characteristics when selecting the referencedata. The reference ECG data may also be indexed according to the ECGlead configuration. If the patient's lead configuration is known, thesecond computer system may select or modify the reference ECG data to bemore appropriate to specific sensor configuration. The reference datamay be tailored to the specific patient and specific sensorconfiguration in order to advantageously improve analysis by the patientmobile device 12.

The reference population data may be used in processing by the secondcomputer system, as described below. The data may include data relatedto patient-specific learning based on past monitoring sessions. Forexample, if a patient has had an intermittent bundle branch block, atemplate and characterization of the bundle branch block beat may becreated by the system 10, and bundle branch block may be moreconfidently detected if it occurred during a new monitoring session.

Having a set of previously observed ECG patterns may advantageously makeit easier to detect new patterns. For example, if a patient previouslyhad bundle branch block, but there is a change in the extent of theblock causing a significantly wider beat morphology, the change may bedetected.

In the event that the mobile patient device detects a new beat type oran abnormality that is not already represented in its memory, the modulemay send the data to the second computer system 24. Doing so may allowthe second computer system 24 to advantageously maintain a completerecord of known ECG findings for the patient.

As with the population data, the patient-specific data may be indexedfor the sensor configuration. The reference patient data, as with thereference population data, may be used by the second computer system 24in processing of the data from the mobile device 12, as described below.

Conditions for In-Depth Analysis

Generally, the mobile device 12 may capture data from the patient andmay process the data. The mobile device may send the data to the secondcomputer system 24 for processing under certain conditions. The mobiledevice may send the data to the second computer system 24, when themobile device 12 encounters an ECG pattern that has not been seen beforeor that has been seen infrequently. The mobile device 12 may send thedata to the second computer system 24 when the mobile device encountersa transition to a new rhythm. For example, whenever the mobile device 12detects an ECG patterns that seems like atrial fibrillation, the mobiledevice 12 may transfer a segment of the ECG data containing the onset ofthe detected pattern to the second computer system 24 for detailedanalysis to determine if the segment fits the pattern of atrialfibrillation and, if it does, to determine the exact onset of the atrialfibrillation. The analysis performed by the second computer system 24may involve detailed processing of the ECG to remove the effect of theQRST complex and to analyze the atrial activity.

Other conditions under which the mobile device 12 sends data to thesecond computer system 24 may include when the mobile device 12 fallsbehind making a determination on a segment of ECG data. The mobiledevice 12 also may send data to the second computer system 24 fortrending or audit purposes at periodic intervals or at pre-specifiedtimes or conditions.

Cooperative processing as described above may allow for contrasting andcomparing mobile device 12 and second computer system 24 results. Forexample, and without limitation, the second computer system 24 may havegreater processing power than a mobile device 12 and, thus, the secondcomputer system 24 may be better suited for in-depth analysis involvingcomputationally intensive pattern recognition. The analysis performed onthe second computer system 24 may compare the ECG against a largedatabase of ECG records and patterns. For example, and withoutlimitation, the database may include the patient's past ECG data andpopulation data. This combination of greater power and larger availablereference data may advantageously permit the second computer system 24to perform rigorous in-depth analysis of the ECG to confirm findings,find new patterns and reject errors. The more powerful computingresources on the second system 24 may allow faster processing than inthe mobile device 12, and less ECG data may need to be processed on thesecond system 24 because the mobile device 12 may only send a portion ofall its findings to the second system 24.

Even though mobile processors are increasingly powerful, in-depthanalysis by the mobile device 12 with existing processors would sufferthe disadvantage of drawing significant power. Such a mobile device 12may require larger batteries or more frequent re-charging, both of whichwould make the mobile device 12 more cumbersome to use. For thenear-term, there exists a practical limitation on how much of theprocessing can be done by the mobile device 12. Likewise, existingwireless network capability, reliability, and cost make it prohibitiveto stream all of the ECG data to the second computer system 24. Even ifit were reasonable to do so, the economics of housing and supportingdedicated second computer systems 24 would likely be unfavorable, andwould require a larger second computer system 24 than the cooperativeprocessing approach described above. Furthermore, there would be anadded cost because the real-time reliability of the second computersystem 24, and network connections would need to feature higherthroughput capacities.

Even as technology progresses and mobile processors become more powerfuland more power efficient, and fuel cell technology provides increased“battery” life, the cooperative processing approach described above willremain useful. One reason is convenience. Patients may want and expectdevices to continue to shrink in size, yet provide increasingfunctionality. Even though it may be possible in future years to doalmost all of the ECG processing in a device that is the size of apresent-day PDA, for instance, patients would likely not want to havedevices that large. This may be especially true of those who arerelatively healthy and are using the device for routine mobilemonitoring as part of a general regimen of keeping healthy.

In a limited computing environment, the processing may be set to findrepresentative events. One example is the problem of categorizing beattype templates under different body positions. Changes in body position(e.g. supine vs. upright) may cause a shift in the position of theheart, which usually changes the shape of the QRS complexes recorded bythe different leads. A QRST template may be indexed by QRS electricalaxis (or modified based on the QRS axis) so that two different QRSTmorphologies, which differ because the heart was in a differentposition, may be determined by the algorithm to be the same beat type.

Referring to FIG. 5, the second computer system 24 may operate by itselfor part of a larger facility including a server-farm. The secondcomputer system 24 may be configured to include a processing manager,remote processor coordinator, and remote ECG processing algorithmmanager. In addition, the second computer system 24 may include apatient data management and also a trend monitoring manager thatinterfaces with and manages the patient database. The second computersystem 24 may include an extended ECG processing algorithm and areference ECG Pattern Matching manager. The second system 24 also mayinclude processes for report generation and event escalation.

The second computer system 24 may be implemented in many different ways.For example, and without limitation, the system 24 may be implemented asa single computer system that is network enabled at a patient's home.Such a private second computer system 24 may maintain a detailed recordof ECG findings over time for the patient, and may be configured toadvantageously escalate specific types of findings by sending data toanother second computer system at a clinical monitoring facility, or bygenerating a fax, e-mail, or other communication artifact to be sent tomedical personnel. A single second computer system may be capable ofconcurrently servicing more than one patient mobile device.

The second computer system 24 at a monitoring facility may be part of alarge facility that can establish a session with the mobile device 12 sothat the same server continues to interact with the mobile device 12.The second computer system at a monitoring facility may be configurableon a per-patient basis to specify escalation rules for different typesof findings.

The second computer system 24 may receive the voice notes from themobile device 12 discussed above. For example, and without limitation,the second computer system may use speech to text technology to producea text note from the speech note generated by the mobile device 12 toassociate with the data. The second computer system software mayproactively request the signals from the patient to develop arepresentative sampling of the ECG over time. The second computer system24 may interact with a database to keep a historical record of part orall of the data that it receives from the mobile device. The secondcomputer system may analyze the historical ECG record to produce beattemplates and rules relevant to the patient.

The second computer system 24 may have access to any of the data in themobile device 12. In addition, the second computer system 24 may send,as appropriate, updated parameters that determine the general operationof the mobile device 12, including the types of episodes that aredetected and reported. The second system may send corrections tointernal classifications or to templates produced and held by the mobiledevice 12. The second system 24 may send reference data including ECGbeat templates, and/or historical data for the patient (for example, andwithout limitation, identifying what is normal for that patient). Thesecond system 24 may send instruction intended for the patient.

Templates

Exemplary templates may be generated for various functions andprocessing performed by the system. The templates may be adjusted andused by the mobile device and the second computer system 24 based onfindings observed during processing of the ECG signals.

Data Acquisition

For example, and without limitation, a subject may wear ECG electrodesor an undergarment with integrated ECG sensors. For routine monitoringof one or two channels of ECG, signals may usually be acquired by theECG module, which may amplify the ECG signal and may filter it topreserve frequencies in the range of 0.05 to 100 Hz. The ECG signal maybe sampled typically at a frequency in the range of 250 to 360 Hz. Thedigitized ECG may be transferred to the mobile processor for processing.The mobile processor may save the data in its flash memory.

Ventricular and Atrial Activity Processing

Ventricular processing may involve some or all of the following actions:

Bandpass filtering of the ECG in a specified frequency range of, forexample, and without limitation, 5-40 Hz to advantageously emphasize thesignal content of the QRST complex;

Multi-channel peak detection to advantageously identify candidate QRScomplexes in the filtered signal;

Measurement of the subject's physical orientation to detect shift inbody position; and

Comparison of detected beats against templates derived from live ECGdata and against reference templates provided by the second computersystem 24.

If the subject's body position has changed, the comparison against thelive ECG templates may consider the change in the QRS axis. The templatemay keep track of QRS shape as a function of the QRS axis.

Reference templates may include QRS axis information. The templatematching may be restricted to templates that have a QRS axis similar tothe current live ECG. Or the reference template may be transformed tomake the QRST axis comparable with the live ECG data.

When a new QRS morphology is found, ECG preceding and following may besent by the mobile device 12 to the second computer system 24 forin-depth analysis. The second computer system 24 may send backinformation that includes templates or parameters that may be used inclassification of QRS morphologies.

Atrial Activity Processing may involve some or all of the followingactions:

Preliminary classification of the beat based on template matching;

Multi-channel P-wave detection to look for individual P-waves precedingQRS complex. Comparing each P-wave against templates derived from recentbeats and against reference templates provided by the second computersystem 24. Producing a measure of each P-wave's significance based onthe template match and a signal-to-noise;

Additional processing (if single P-wave is not found and if the deviceis keeping up with real-time signals) may involve some or all of thefollowing actions:

Adaptively subtracting the QRST complex by subtracting out the matchingQRST template; and

P-wave detection in the QRST interval to identify a P-wave.

Beat Classification

Beat classification may determine whether a beat is of atrial,junctional or ventricular origin. The processing may involve some or allof the following actions:

Producing timing and morphology measures of the beat;

Measuring how well the measures of (P)QRST morphology and timingmeasures match predefined parameters sets;

Default parameters sets based on conventional clinical definitions ofECG analysis;

Customized parameter sets may be supplied by the second computer system24 based on past ECG or based on other data obtained from the subject.These may replace or augment the default parameter sets;

Measuring the correlation of an observed (P)QRST to (P)QRST templates.These measures may include the following: Similarity of the QRST to anexisting QRST template cluster that may be either derived from the liveECG data or provided by the second computer system 24 based on priorECGs; Similarity of the P-wave (if one has been detected) to an existingP-wave template cluster and whether that template is associated with theQRST cluster; Whether the (P)QRST template cluster has been definitivelyclassified as atrial, junctional or ventricular origin; and

Measuring how well a (P)QRST for the most recent N beats matchespreviously observed patterns for N beats.

The matching may include absolute and heart-rate-normalized timingintervals of the QRS complexes and P-waves, along with the clusteringmeasures of the QRS complexes and P-waves.

The number (N) of beats compared may range from 2 to 8 or more based onhow well the processing is able to keep up with the real-time signals.

The second computer system 24 may provide the mobile processor withpreviously classified multi-beat patterns to use as reference data. Themobile processor may limit the number of patterns compared based on theavailable processing time.

Updating of templates based on the classification to include thecontribution of QRS complexes put into the template cluster. Clustersmay be produced, merged or classified based on rules in the mobileprocessing algorithm or based on information sent by the second computersystem 24.

Producing an audit trail that can be used (if necessary) by the secondcomputer system 24 to review the decisions made by the mobile processor.

Rhythm Classification

Rhythm classification may involve looking for clinically recognizedrhythm patterns in a sequence of beats. Multiple rhythm classificationsmay be determined for a set of beats, each classification having anassociated confidence measure and clinical severity.

Rhythm and Event Matching (Hypothesis Testing)

The observed sequence of beats and their associated measures may becompared against reference data. For example, and without limitation,the reference data may include patterns derived from a generalpopulation database. Also for example, and without limitation, thereference data may include patterns identified for the subject. Thecomparison may be based on a set of vectors including measurements ofthe P-waves, atrial activity and QRST complexes that comprise thesequence of beats.

Implementations

The invention may be implemented in digital electronic circuitry, or incomputer hardware, firmware, software, or in combinations thereof. Anapparatus of an embodiment of the invention may be implemented in acomputer program product tangibly embodied in a machine-readable storagedevice for execution by a programmable processor. Method actions may beperformed by a programmable processor executing a program ofinstructions to perform functions of the invention by operating on inputdata and generating output.

The invention may be implemented advantageously in one or more computerprograms that are executable on a programmable system including at leastone programmable processor coupled to receive data and instructionsfrom, and to transmit data and instructions to, a data storage system,at least one input device, and at least one output device. Each computerprogram can be implemented in a high-level procedural or object orientedprogramming language, or in assembly or machine language if desired; andin any case, the language can be a compiled or interpreted language.

Suitable processors may include, by way of example, both general andspecial purpose microprocessors. Generally, a processor may receiveinstructions and data from a read-only memory and/or a random accessmemory. Generally, a computer may include one or more mass storagedevices for storing data files; such devices include magnetic disks,such as internal hard disks and removable disks; magneto-optical disks;and optical disks. Storage devices suitable for tangibly embodyingcomputer program instructions and data include all forms of non-volatilememory, including by way of example semiconductor memory devices, suchas EPROM, EEPROM, and flash memory devices; magnetic disks such asinternal hard disks and removable disks; magneto-optical disks; andCD_ROM disks. Any of the foregoing may be supplemented by, orincorporated in, ASICs (application-specific integrated circuits).

Some of the illustrative aspects of the present invention may beadvantageous in solving the problems herein described and other problemsnot discussed which are discoverable by a skilled artisan.

While the above description contains much specificity, these should notbe construed as limitations on the scope of any embodiment, but asexemplifications of the presented embodiments thereof. Many otherramifications and variations are possible within the teachings of thevarious embodiments. While the invention has been described withreference to exemplary embodiments, it will be understood by thoseskilled in the art that various changes may be made and equivalents maybe substituted for elements thereof without departing from the scope ofthe invention. In addition, many modifications may be made to adapt aparticular situation or material to the teachings of the inventionwithout departing from the essential scope thereof. Therefore, it isintended that the invention not be limited to the particular embodimentdisclosed as the best or only mode contemplated for carrying out thisinvention, but that the invention will include all embodiments fallingwithin the scope of the appended claims. Also, in the drawings and thedescription, there have been disclosed exemplary embodiments of theinvention and, although specific terms may have been employed, they areunless otherwise stated used in a generic and descriptive sense only andnot for purposes of limitation, the scope of the invention therefore notbeing so limited. Moreover, the use of the terms first, second, etc. donot denote any order or importance, but rather the terms first, second,etc. are used to distinguish one element from another. Furthermore, theuse of the terms a, an, etc. do not denote a limitation of quantity, butrather denote the presence of at least one of the referenced item.

Thus the scope of the invention should be determined by the appendedclaims and their legal equivalents, and not by the examples given.

That which is claimed is:
 1. An arrangement comprising: a first computersystem for processing ECG data representative of a beating heart, thefirst computer system comprising: a physiological sensor adapted tocollect the ECG data, and a mobile patient device adapted to receive theECG data from the physiological sensor and execute a process for usingat least one pattern to detect a notable finding in the ECG data; asecond computer system adapted to execute a process for analyzing thenotable finding, for determining at least one new pattern to send to thefirst computer system, and for sending the at least one new pattern tothe first computer system; and a communication link for sending thenotable finding from the first computer system to the second computersystem and the at least one new pattern from the second computer systemto the first computer system; wherein the at least one new patterncomprises a rule that includes a set of conditions and an action toperform if the set of conditions is met; and wherein use of the secondcomputer system reduces the amount of processing required by the firstcomputer system.
 2. The arrangement according to claim 1 wherein the atleast one new pattern includes parameters of a mathematical model. 3.The arrangement according to claim 1 wherein the process for determiningthe at least one new pattern includes deriving a template fromhistorical ECG data.
 4. The arrangement according to claim 1 wherein theprocess to send the at least one new pattern includes sending softwareto the first computer system.
 5. The arrangement according to claim 1wherein the at least one new pattern includes a cardiac rhythm pattern.6. The arrangement according to claim 1 wherein the process fordetermining the at least one new pattern includes selecting usercharacteristics from at least one of patient clinical information anddemographic information, and wherein the process to send the at leastone new pattern includes sending the user characteristics to initializethe first computer system.
 7. A method comprising: collecting ECG datausing a physiological sensor; providing the ECG data to a mobile patientdevice configured to analyze ECG data representative of a beating heart;receiving a first notable finding from the mobile patient device;analyzing the first notable finding using a first pattern to detect asecond notable finding, wherein the analysis is performed on a deviceother than the mobile patient device to reduce the processing burden ofthe mobile patient device; determining a second pattern to send to themobile patient device based on the second notable finding; and sendingthe second pattern to the mobile patient device.
 8. The method accordingto claim 7 wherein the second pattern includes parameters of amathematical model.
 9. The method according to claim 7 furthercomprising: comparing the analyzed ECG data to templates from categoriesof cardiac events in order to determine the first notable finding. 10.The method according to claim 7 wherein the second pattern comprises arule, the method further comprising generating the rule for a specificcondition.
 11. The method according to claim 7 wherein the secondpattern comprises a rule, the method further comprising generating therule for a specific patient.
 12. The method according to claim 7 whereinthe second pattern includes a template derived from historical ECG data.13. The method according to claim 7 wherein the second pattern includessoftware.
 14. The method according to claim 7 wherein the second patternincludes a cardiac rhythm pattern.
 15. The method according to claim 7wherein the second pattern includes user characteristics selected frompatient clinical information and demographic information, and whereinsending the second pattern further comprises sending the usercharacteristics to initialize the mobile patient device.